Mapping Part 2 — Workbook¶

In this lesson, we’re going to learn how to analyze and visualize geographic data.

Torn Apart / Separados¶

Read in Data¶

The data in this section was drawn from Torn Apart / Separados Project. It maps the locations of Immigration and Customs Enforcement (ICE) detention facilities, as featured in Volume 1.

import pandas as pd
pd.options.display.max_rows = 100
pd.options.display.max_columns = 100

You can find this data on GitHub: https://github.com/xpmethod/torn-apart-open-data/blob/master/iceFacs.csv

fac_df = pd.read_csv("../data/ICE-facilities.csv")
# Drop any blank lat, lon coordinates
fac_df = fac_df.dropna(subset=['lat', 'lon'])
fac_df

	lat	lon	adpSum	onWeb	Flags	fulladdr	DETLOC	Name	Address	City	County	State	Zip	Circuit	AOR	Docket	Type	Type.Detailed	ICE.Funded	Population.Count	Date.of.Last.Use	Date.of.First.Use	FY18.Max.Population.Count	FY18.ADP	FY17.ADP	FY16.ADP	FY15.ADP	FY14.ADP	FY18.Facility.Bookins	FY17.Facility.Bookins	FY16.Facility.Bookins	FY15.Facility.Bookins	FY18.Non.Criminal	FY18.Criminal	FY17.Non.Criminal	FY17.Criminal	FY16.Non.Criminal	FY16.Criminal	FY15.Non.Criminal	FY15.Criminal	ICE.Threat.Level.1	ICE.Threat.Level.2	ICE.Threat.Level.3	No.ICE.Threat.Level	Facility.Operator	FY17.Calendar.Days.in.Use	FY17...of.Days.in.Use	FY17.Total.Mandays	FY17.Max.Pop.Count	geocodelat	geocodelon
0	28.895000	-99.121200	8391	28.8950	NaN	566 VETERANS DRIVE PEARSALL TX 78061	STCDFTX	SOUTH TEXAS DETENTION COMPLEX	566 VETERANS DRIVE	PEARSALL	FRIO	TX	78061	5	SNA	STC	CDF	CDF	Yes	1735	04.11.2017	27.06.2005	1811	1754	1640	1728	1547	1722	2056	13065	15317	15513	1182	573	1120	520	1115	613	944	603	145	112	311	1187	GEO	372	1.02	598554	1854	28.896498	-99.116863
1	32.036600	-84.771800	8004	32.0366	NaN	146 CCA ROAD LUMPKIN GA 31815	STWRTGA	STEWART DETENTION CENTER	146 CCA ROAD	LUMPKIN	STEWART	GA	31815	11	ATL	ATL	IGSA	DIGSA	Yes	1917	04.11.2017	01.09.2000	1947	1802	1840	1369	1374	1619	2379	11022	7583	9455	666	1136	861	979	585	785	363	1011	423	344	365	671	CCA	372	1.02	671515	1992	32.037982	-84.772465
2	34.559200	-117.441000	7265	34.5592	NaN	10250 RANCHO ROAD ADELANTO CA 92301	ADLNTCA	ADELANTO ICE PROCESSING CENTER	10250 RANCHO ROAD	ADELANTO	SAN BERNARDINO	CA	92301	9	LOS	LOS	IGSA	DIGSA	Yes	1747	04.11.2017	29.08.2011	1767	1691	1713	1472	1180	1209	1870	8623	10531	8103	721	970	874	840	626	846	237	942	595	206	164	726	GEO	372	1.02	625414	1918	34.557721	-117.442524
3	32.817700	-111.520000	7096	32.8177	NaN	1705 EAST HANNA RD. ELOY AZ 85131	EAZ	ELOY FEDERAL CONTRACT FACILITY	1705 EAST HANNA RD.	ELOY	PINAL	AZ	85131	9	PHO	EAZ	IGSA	DIGSA	Yes	1436	04.11.2017	10.03.1991	1444	1401	1378	1433	1401	1483	1310	6873	6254	7079	780	621	759	619	742	691	711	690	230	154	232	785	CCA	372	1.02	502952	1489	32.821231	-111.549772
4	47.249100	-122.421000	6757	47.2491	NaN	1623 E. J STREET TACOMA WA 98421	CSCNWWA	NORTHWEST DETENTION CENTER	1623 E. J STREET	TACOMA	PIERCE	WA	98421	9	SEA	TAC	CDF	CDF	Yes	1421	04.11.2017	07.06.2001	1438	1391	1423	1411	1132	1400	1556	5961	6019	5207	690	701	655	768	682	729	452	680	358	166	174	693	GEO	372	1.02	519386	1563	47.250214	-122.422746
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
479	42.055299	-104.952540	1	0.0000	NaN	850 MAPLE STREET WHEATLAND WY 82201	PLATTWY	PLATTE COUNTY JAIL	850 MAPLE STREET	WHEATLAND	PLATTE	WY	82201	10	DEN	DEN	USMS IGA	USMS IGA	Yes	3	04.11.2017	04.04.2000	5	1	0	0	0	0	9	45	53	23	0	1	0	0	0	0	0	0	0	0	0	0	COUNTY (SHERIFF)	61	0.17	90	5	42.055299	-104.952540
480	31.793411	-106.369373	1	0.0000	NaN	8915 MONTANA ST. EL PASO TX 79925	EPCJUVI	EL PASO JUVENILE	8915 MONTANA ST.	EL PASO	EL PASO	TX	79925	5	ELP	EPC	Other	HOLD	Yes	1	04.11.2017	27.10.1998	17	1	0	0	0	0	565	915	11	17	1	0	0	0	0	0	0	0	0	0	0	1	FEDERAL	59	0.16	61	9	31.793411	-106.369373
481	39.671492	-75.714329	1	0.0000	NaN	970 BROAD STREET NEWARK NJ 7102	NEWHOLD	NEW/INS OS HOLD ROOM	970 BROAD STREET	NEWARK	ESSEX	NJ	7102	3	NEW	NEW	Other	HOLD	Yes	0	20.10.2017	01.01.1988	2	1	0	0	0	0	4	102	209	264	1	0	0	0	0	0	0	0	0	0	0	1	FEDERAL	37	0.10	50	2	39.671492	-75.714329
482	26.204563	-98.270145	1	0.0000	NaN	BENTSEN TOWER, 1701 W BUS HWY 83 MCALLEN TX 78501	USMS3TX	US MARSHALS (SOUTH DISTRICT, TEXAS)	BENTSEN TOWER, 1701 W BUS HWY 83	MCALLEN	HIDALGO	TX	78501	5	SNA	PIC	USMS IGA	USMS IGA	Yes	0	02.05.2016	26.06.2005	0	0	0	0	1	0	0	2	1	2	0	0	0	0	0	0	0	1	0	0	0	0	FEDERAL	0	0.00	0	0	26.204563	-98.270145
483	41.528728	-73.363545	1	0.0000	NaN	BRIDGEWATER STATE HOSPITAL BRIDGEWATER MA 2324	MABSHOS	BRIDGEWATER STATE HOSPITAL	BRIDGEWATER STATE HOSPITAL	BRIDGEWATER	PLYMOUTH	MA	2324	1	BOS	BOS	Other	HOSPITAL	No	0	11.06.2015	09.11.2000	0	0	0	0	1	0	0	1	0	3	0	0	0	0	0	0	0	0	0	0	0	0	HOSPITAL	0	0.00	0	0	41.528728	-73.363545

318 rows × 51 columns

Import Folium¶

!pip install folium

import folium

Let’s establish a base map

US_map = folium.Map(location=[42, -102], zoom_start=4)
US_map

Add a Circle Marker¶

There are a few different kinds of markers that we can add to a Folium map, including circles. To make a circle, we can call folium.CircleMarker() with a particular radius and the option to fill in the circle.

You can explore more customization options in the Folium documentation. We’re also going to add a hover tooltip.

# Establish base map
US_map = folium.Map(location=[42, -102], zoom_start=4)

# Loop through rows in the Pandas DataFrame
for index, row in fac_df.iterrows():
    
    # Make a caption
    caption_info = f"""{row['Name']} <br>
                    {row['City']}, {row['State']}""" 
    
    folium.CircleMarker(
                # Latitude, longitude for each marker
                location=[row['lat'], row['lon']],
        
                # Size, fill, color of the circle marker
                radius = 10, fill = True, color='orange',
        
                # Text that goes into the popup or tooltip
                tooltip = caption_info 
                  
                 ).add_to(US_map)

US_map

Your Turn!¶

What is this map showing us about ICE (U.S Immigration and Customs Enforcement) that words alone or even a DataFrame couldn’t capture?
What other information might be important to have in the popup or tooltip?
What other design choices would you make to improve this map? What consequences could these decisions have in the world?

Pick at least one other category from the DataFrame and include it in the popup or toolip. You can review the existing columns in the DataFrame below.

fac_df.columns

fac_df.sample(5)

	lat	lon	adpSum	onWeb	Flags	fulladdr	DETLOC	Name	Address	City	County	State	Zip	Circuit	AOR	Docket	Type	Type.Detailed	ICE.Funded	Population.Count	Date.of.Last.Use	Date.of.First.Use	FY18.Max.Population.Count	FY18.ADP	FY17.ADP	FY16.ADP	FY15.ADP	FY14.ADP	FY18.Facility.Bookins	FY17.Facility.Bookins	FY16.Facility.Bookins	FY15.Facility.Bookins	FY18.Non.Criminal	FY18.Criminal	FY17.Non.Criminal	FY17.Criminal	FY16.Non.Criminal	FY16.Criminal	FY15.Non.Criminal	FY15.Criminal	ICE.Threat.Level.1	ICE.Threat.Level.2	ICE.Threat.Level.3	No.ICE.Threat.Level	Facility.Operator	FY17.Calendar.Days.in.Use	FY17...of.Days.in.Use	FY17.Total.Mandays	FY17.Max.Pop.Count	geocodelat	geocodelon
472	42.372992	-83.018564	1	0.0000	NaN	5555 CONNER AVENUE, SUITE 3 NORTH DETROIT MI 48213	SAMBCMI	SAMARITAN BEHAVIORAL CENTER	5555 CONNER AVENUE, SUITE 3 NORTH	DETROIT	COUNTY	MI	48213	6	DET	NaN	Other	HOSPITAL	No	1	04.11.2017	27.03.2013	1	1	0	0	0	0	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0	1	NaN	93	0.25	86	1	42.372992	-83.018564
451	31.803547	-106.288678	1	0.0000	NaN	11541 MONTANA AVE EL PASO TX 79936	IEPHOLD	ICE EL PASO HOLD ROOM	11541 MONTANA AVE	EL PASO	EL PASO	TX	79936	5	ELP	NaN	Other	HOLD	Yes	0	22.10.2017	05.07.2016	2	1	0	0	0	0	35	419	40	0	1	0	0	0	0	0	0	0	0	0	0	1	NaN	141	0.39	136	2	31.803547	-106.288678
334	40.192202	-75.914124	9	0.0000	NaN	1000 ACADEMY DRIVE MORGANTOWN PA 19543	ABRXSPA	ABRAXAS ACADEMY DETENTION CENTER	1000 ACADEMY DRIVE	MORGANTOWN	BERKS	PA	19543	3	PHI	BRK	IGSA	JUVENILE	Yes	2	04.11.2017	23.05.2014	2	2	3	4	0	0	2	7	10	3	1	1	2	2	2	2	0	0	1	0	0	1	NaN	372	1.02	1207	5	40.192202	-75.914124
120	43.218700	-70.935100	283	43.2187	NaN	266 COUNTY FARM ROAD DOVER NH 3820	STRAFNH	STRAFFORD COUNTY CORRECTIONS	266 COUNTY FARM ROAD	DOVER	STRAFFORD	NH	3820	1	BOS	BOS	IGSA	IGSA	Yes	56	04.11.2017	11.02.1991	87	65	95	67	29	27	175	888	471	517	20	45	33	62	24	43	10	18	27	8	9	20	COUNTY (CORRECTIONS)	372	1.02	34578	130	43.218476	-70.940417
361	34.783840	-91.901281	6	0.0000	NaN	203 W. FRONT STREET LONOKE AR 72086	LONPDAR	LONOKE POLICE DEPARTMENT	203 W. FRONT STREET	LONOKE	LONOKE	AR	72086	8	NOL	FSA	IGSA	IGSA	Yes	1	04.11.2017	12.03.2002	11	2	1	1	1	1	58	292	202	260	0	1	0	1	0	1	0	1	0	1	0	0	CITY	185	0.51	524	15	34.783840	-91.901281

Add another category to the tooltip/popup

# Establish base map
US_map = folium.Map(location=[42, -102], zoom_start=4)

# Loop through rows in the Pandas DataFrame
for index, row in fac_df.iterrows():
    
    # Make a caption -- <br> is HTML for a line break
    caption_info = f"""{row['Name']} <br>
                    {row['City']}, {row['State']} <br>
                    More info here: """ 
                    # ☝️ Add another category into the caption above and/or alter the design! ☝️

    folium.CircleMarker(
                # Latitude, longitude for each marker
                location=[row['lat'], row['lon']],
        
                # Size, fill, color of the circle marker
                radius = 10, fill = True, color='orange',
        
                # Text that goes into the popup or tooltip
                tooltip = caption_info 
                  
                 ).add_to(US_map)

US_map

Save the map as an HTML file

US_map.save("ICE-map.html")

Choropleth Maps¶

Choropleth map = a map where areas are shaded according to a value

The data in this section was drawn from Torn Apart / Separados Project. This data maps the “cumulative ICE awards since 2014 to contractors by congressional district,” as featured in Volume 2.

To create a chropleth map with Folium, we need to pair a “geo.json” file (which indicates which parts of the map to shade) with a CSV file (which includes the variable that we want to shade by).

The following data was drawn from the Torn Apart / Separados project

First, we will examine the geoJSON file. To learn more about geoJSON, you can explore here: https://geojson.io/#map=13/42.4349/-76.5028

geojson_df = pd.read_json("../data/ICE_money_districts.geo.json")

geojson_df['features'][0]

{'type': 'Feature',
 'geometry': {'type': 'MultiPolygon',
  'coordinates': [[[[-87.166581, 31.519561],
     [-87.135051, 31.642417000000002],
     [-87.0511, 31.71834],
     [-86.905899, 31.753035],
     [-86.906899, 31.830628],
     [-86.908939, 31.961673],
     [-86.857583, 31.962167],
     [-86.448198, 31.964629],
     [-86.405005, 31.963775],
     [-86.406276, 32.050731],
     [-86.408272, 32.208976],
     [-86.300769, 32.219544],
     [-86.307426, 32.307219],
     [-86.229056, 32.315688],
     [-86.299216, 32.326703],
     [-86.322056, 32.378075],
     [-86.436067, 32.331314],
     [-86.484678, 32.284473],
     [-86.474479, 32.331547],
     [-86.496774, 32.344437],
     [-86.653419, 32.397247],
     [-86.781354, 32.392494],
     [-86.816107, 32.30997],
     [-86.814912, 32.340803],
     [-86.906403, 32.536712],
     [-86.917595, 32.664169],
     [-86.71339, 32.661732],
     [-86.714219, 32.705694],
     [-86.413116, 32.707386],
     [-86.413335, 32.750591],
     [-86.374974, 32.75358],
     [-86.336694, 32.76813],
     [-86.31948, 32.753698],
     [-86.007187, 32.754984],
     [-85.87986, 32.754528],
     [-85.886148, 32.493053],
     [-85.852625, 32.475747],
     [-86.023012, 32.419978],
     [-86.046402, 32.406146],
     [-86.192284, 32.43613],
     [-86.19919, 32.382142],
     [-86.13857, 32.367823],
     [-86.190647, 32.291901],
     [-86.118065, 32.223873],
     [-86.112586, 32.15589],
     [-85.997859, 32.141605],
     [-85.999157, 32.250543],
     [-85.986557, 32.272342],
     [-85.919293, 32.274382],
     [-85.898689, 32.274987],
     [-85.898539, 32.305289],
     [-85.856218, 32.231975],
     [-85.433543, 32.234648],
     [-85.427441, 32.146551],
     [-85.410241, 32.146651],
     [-85.257834, 32.147931],
     [-85.185067, 32.061708],
     [-85.0514111279128, 32.062256083622],
     [-85.06359054298468, 31.991857],
     [-85.067829, 31.967358],
     [-85.114031, 31.89336],
     [-85.141831, 31.839261],
     [-85.1291593155832, 31.7802782680766],
     [-85.1254405773425, 31.7629687034183],
     [-85.11893, 31.732664],
     [-85.12553, 31.694965],
     [-85.058169, 31.620227],
     [-85.05796, 31.57084],
     [-85.041881, 31.544684],
     [-85.0516814295062, 31.519540329141698],
     [-85.071621, 31.468384],
     [-85.066005, 31.431363],
     [-85.092487, 31.362881],
     [-85.087929, 31.321648],
     [-85.08882996353049, 31.308647755496597],
     [-85.089774, 31.295026],
     [-85.108192, 31.258591],
     [-85.107516, 31.186451],
     [-85.035615, 31.108192],
     [-85.0211075623628, 31.0754638723064],
     [-85.011392, 31.053546],
     [-85.002499, 31.000682],
     [-85.031285, 31.000647],
     [-85.145959, 31.000693],
     [-85.333319, 30.999555],
     [-85.4882982889391, 30.9979646232873],
     [-85.4980015850895, 30.997865049361398],
     [-85.57949757633828, 30.9970287484109],
     [-85.749715, 30.995282],
     [-85.893632, 30.993455],
     [-86.0350381049506, 30.9937496073023],
     [-86.1872480489813, 30.9940667234622],
     [-86.364974, 30.994437],
     [-86.3886446620004, 30.9945282152752],
     [-86.563494, 30.995202],
     [-86.68824096424609, 30.996201890001498],
     [-86.700251, 31.008901],
     [-86.700282, 31.192217],
     [-86.772519, 31.202243],
     [-86.763961, 31.261293],
     [-87.427455, 31.260386],
     [-87.166581, 31.519561]]]]},
 'properties': {'state': 'Alabama',
  'districtNumber': 2,
  'districtName': 'ta-ordinal-nd-m',
  'party': 'republican',
  'profiteer': {'name': 'MMI Outdoor', 'value': 38576},
  'dom_id': 'district-alabama-2',
  'representative': 'Martha Roby',
  'representative_photo_url': 'https://upload.wikimedia.org/wikipedia/commons/5/55/Martha_roby_113_congressional_portrait_%28cropped%29.jpg',
  'total_value': 38577.4,
  'district_url': 'https://en.wikipedia.org/wiki/Alabama%27s_2nd_congressional_district'}}

districts_df = pd.read_csv("../data/ICE_money_districts.csv")
districts_df = districts_df.dropna(subset=['districtName', 'representative'])
districts_df

	id	id2	state	districtNumber	districtName	party	district_url	representative	representative_photo_url	total_awards
0	5001500US0101	101	Alabama	1	ta-ordinal-st-m	republican	https://en.wikipedia.org/wiki/Alabama%27s_1st_congressional_district	Bradley Byrne	https://upload.wikimedia.org/wikipedia/commons/7/71/Rep_Bradley_Byrne_%28cropped%29.jpg	0.00
1	5001500US0102	102	Alabama	2	ta-ordinal-nd-m	republican	https://en.wikipedia.org/wiki/Alabama%27s_2nd_congressional_district	Martha Roby	https://upload.wikimedia.org/wikipedia/commons/5/55/Martha_roby_113_congressional_portrait_%28cropped%29.jpg	38577.40
2	5001500US0103	103	Alabama	3	ta-ordinal-rd-m	republican	https://en.wikipedia.org/wiki/Alabama%27s_3rd_congressional_district	Mike Rogers	https://upload.wikimedia.org/wikipedia/commons/e/ee/Mike_Rogers_official_photo_%28cropped%29.jpg	0.00
3	5001500US0104	104	Alabama	4	ta-ordinal-th-m	republican	https://en.wikipedia.org/wiki/Alabama%27s_4th_congressional_district	Robert Aderholt	https://upload.wikimedia.org/wikipedia/commons/9/9f/Rep._Robert_B._Aderholt_%28cropped%29.jpg	171873.55
4	5001500US0105	105	Alabama	5	ta-ordinal-th-m	republican	https://en.wikipedia.org/wiki/Alabama%27s_5th_congressional_district	Mo Brooks	https://upload.wikimedia.org/wikipedia/commons/b/b6/Mo_Brooks_Portrait_%28cropped%29.jpg	40346.00
...	...	...	...	...	...	...	...	...	...	...
432	5001500US5506	5506	Wisconsin	6	ta-ordinal-th-m	republican	https://en.wikipedia.org/wiki/Wisconsin%27s_6th_congressional_district	Glenn Grothman	https://upload.wikimedia.org/wikipedia/commons/1/16/Glenn_Grothman_official_congressional_photo_%28cropped%29.jpg	3242401.61
433	5001500US5507	5507	Wisconsin	7	ta-ordinal-th-m	republican	https://en.wikipedia.org/wiki/Wisconsin%27s_7th_congressional_district	Sean Duffy	https://upload.wikimedia.org/wikipedia/commons/d/d7/Sean_Duffy_official_congressional_photo_%28cropped%29.jpg	32698.55
434	5001500US5508	5508	Wisconsin	8	ta-ordinal-th-m	republican	https://en.wikipedia.org/wiki/Wisconsin%27s_8th_congressional_district	Mike Gallagher	https://upload.wikimedia.org/wikipedia/commons/a/ad/Mike_Gallagher_Official_Portrait_2017_%28cropped%29.png	237392.73
435	5001500US5600	5600	Wyoming	0	ta-at-large-district	republican	https://en.wikipedia.org/wiki/Wyoming%27s_at-large_congressional_district	Liz Cheney	https://upload.wikimedia.org/wikipedia/commons/d/dd/Liz_Cheney_official_portrait.jpg	0.00
436	5001500US7298	7298	Puerto Rico	98	ta-ordinal-th-m	no-rep	https://en.wikipedia.org/wiki/Puerto_Rico%27s_at-large_congressional_district	Resident Commissioner Jenniffer González	https://upload.wikimedia.org/wikipedia/commons/thumb/7/7a/Official_portrait_of_Resident_Commissioner_Jenniffer_Gonzalez.jpg/800px-Official_portrait_of_Resident_Commissioner_Jenniffer_Gonzalez.jpg	323102.10

431 rows × 10 columns

Your Turn!¶

Run the following cells and inspect the maps. Then answer the discussion questions at the bottom.

Here we are creating a choropleth map by pairing geoJSON data and a Pandas DataFrame.

US_map = folium.Map(location=[42, -102], zoom_start=4)

choro_map = folium.Choropleth(
    # Geojson data
    geo_data = "../data/ICE_money_districts.geo.json",
    # DataFrame data
    data = districts_df,
    # Columns to include from DataFrame
    columns = ['districtName', 'total_awards'],
    # Shared category from geojson
    key_on = 'feature.properties.districtName',
    # Color palette
    fill_color = 'GnBu',
    line_opacity = 0.2,
    legend_name= 'Total ICE Money Received'
).add_to(US_map)

US_map

Discussion¶

To see how our map compares to the Torn Apart / Separados map, you can explore here: http://xpmethod.columbia.edu/torn-apart/volume/2/

What does this map (or the Torn Apart/Separados map) show us about ICE that is different from the previous map?
How do the design and tooltip choices of this map influence the argument being made?

Introduction to Cultural Analytics & Python

Mapping Part 2 — Workbook¶

Torn Apart / Separados¶

Read in Data¶

Import Folium¶

Add a Circle Marker¶

Your Turn!¶

Choropleth Maps¶

Your Turn!¶

Add a Tooltip to Choropleth¶

Discussion¶